Premise As plant lineages diversify across environmental gradients, species are predicted to encounter divergent biotic pressures. This study investigated the evolution of volatile secondary metabolism across species of Helianthus. Methods Leaves and petals of 40 species of wild Helianthus were analyzed via gas chromatography–mass spectrometry to determine volatile secondary metabolite profiles. Results Across all species, 500 compounds were identified; 40% were sesquiterpenes, 18% monoterpenes, 3% diterpenes, 4% fatty acid derivatives, and 35% other compounds such as phenolics and small organic molecules. Qualitatively, annuals and species from more arid western climates had leaf compositions with a higher proportion of total monoterpenes, while erect perennials and species from more mesic eastern habitats contained a higher proportion of total sesquiterpenes. Among species, mass‐based leaf monoterpene and sesquiterpene abundance were identified as largely orthogonal axes of variation by principal component analysis. Profiles for leaves were not strongly correlated with those of petals. Conclusions Volatile metabolites were highly diverse among wild Helianthus, indicating the value of this genus as a model system and rich genetic resource. The independence of leaf and petal volatile profiles indicates a low level of phenotypic integration between vegetative and reproductive structures, implying vegetative defense and reproductive defense or pollinator attraction functions mediated by terpene profiles in these two organs can evolve without major trade‐offs. The major biosynthetic pathways for the major terpenes in wild Helianthus are already well described, providing a road map to deeper inquiry into the drivers of this diversity.

Premise of the study: Plant functional traits are often used to describe spectra of ecological strategies among species. Here we demonstrate a machine learning approach for identifying the traits that contribute most to interspecific phenotypic divergence in multivariate trait space. Methods: Descriptive and predictive machine learning approaches were applied to trait data for the genus Helianthus, including Random Forest and Gradient Boosting Machine classifiers, Recursive Feature Elimination, and the Boruta algorithm. These approaches were applied at the genus level as well as within each of the three major clades within the genus to examine the variability in major axes of trait divergence in three independent species radiations. Key Results: Machine learning models were able to predict species identity from functional traits with high accuracy, and differences in functional trait importance were observed between the genus level and clade levels indicating different axes of phenotypic divergence. Conclusions: Applying machine-learning approaches to identify divergent traits can provide insights into the predictability or repeatability of evolution through comparison of parallel diversification of clades within a genus. These approaches can be implemented in a range of contexts across basic and applied plant science from interspecific divergence to intraspecific variation across time, space, and environmental conditions.

Premise: Here we demonstrate the application of interpretable machine learning methods to investigate intraspecific functional trait divergence using diverse genotypes of the wide-ranging sunflower Helianthus annuus occupying populations across contrasting ecoregions - the Great Plains versus the North American Deserts. Methods: Recursive feature elimination was applied to functional trait data from the HeliantHome database, followed by the application of Boruta to detect traits most predictive of ecoregion. Random Forest and Gradient Boosting Machine classifiers were then trained and validated, with results visualized using accumulated local effects plots. Key Results: The most ecoregion-predictive functional traits span categories of leaf economics, plant architecture, reproductive phenology, and floral and seed morphology. Relative to the Great Plains, genotypes from the North American Deserts exhibit shorter stature, fewer leaves, higher leaf nitrogen, and longer average length of phyllaries. Conclusions: This approach readily identifies traits predictive of ecoregion origin, and thus functional traits most likely to be responsible for contrasting ecological strategies across the landscape. This type of approach can be used to parse large plant trait datasets in a wide range of contexts, including explicitly testing the applicability of interspecific paradigms at intraspecific scales.